{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Week4 进阶作业"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**问题描述**   \n",
    "一、数据说明： Capital Bikeshare （美国Washington, D.C.的一个共享单车公司）提供的共享单车数据。数据包含每天的日期、天气等信息，需要预测每天的共享单车骑行量。 \n",
    "\n",
    "**解题提示**  \n",
    "原始数据集地址：http://archive.ics.uci.edu/ml/datasets/Bike+Sharing+Dataset\n",
    "\n",
    "1) 文件说明\n",
    "day.csv: 按天计的单车共享次数（作业只需使用该文件）  \n",
    "hour.csv: 按小时计的单车共享次数（无需理会）  \n",
    "readme：数据说明文件  \n",
    "\n",
    "2) 字段说明\n",
    "Instant记录号  \n",
    "Dteday：日期  \n",
    "Season：季节（1=春天、2=夏天、3=秋天、4=冬天）  \n",
    "yr：年份，(0: 2011, 1:2012)  \n",
    "mnth：月份( 1 to 12)  \n",
    "hr：小时 (0 to 23) （只在hour.csv有，作业忽略此字段）  \n",
    "holiday：是否是节假日（0/1）  \n",
    "weekday：星期中的哪天，取值为0～6  \n",
    "workingday：是否工作日（0/1）  \n",
    "1=工作日 （是否为工作日，1为工作日，0为非周末或节假日）  \n",
    "weathersit：天气（1：晴天，多云 ",
    "2：雾天，阴天 ",
    "3：小雪，小雨 ",
    "4：大雨，大雪，大雾）  \n",
    "temp：气温摄氏度  \n",
    "atemp：体感温度  \n",
    "hum：湿度  \n",
    "windspeed：风速  \n",
    "casual：非注册用户贡献的骑行量（作业无需理会该字段）  \n",
    "registered：注册用户贡献的骑行量（作业无需理会该字段）  \n",
    "cnt：给定日期（天, day.csv）时间（每小时,hour.csv）总租车人数，响应变量y\n",
    "  \n",
    "casual、registered和cnt三个特征均为要预测的y（cnt =casual+registered ），作业里只需对cnt进行预测。  \n",
    "\n",
    "作业格式要求：\n",
    "1.提交jupyter notebook生成的可执行文件（.ipynb文件）；数据探索，特征工程，模型训练三个可执行文件分别提交。\n",
    "2.在模型训练的可执行文件内，需要按照内容要求进行文字说明，文字说明建议使用Markdown格式。\n",
    "注：也可提交上述的三份可执行文件，或者pycharm生成的包含源码的.py文件。同时将输出结果截图，和文字说明一起整理成一份word文档或者markdown格式的文档，或者输出为pdf，并提交。  \n",
    "\n",
    "**批改标准**  \n",
    "1. 对数据做数据探索分析（可参考EDA_BikeSharing.ipynb，不计分）\n",
    "2. 适当的特征工程（可参考FE_BikeSharing.ipynb，不计分）\n",
    "3. 对全体数据，随机选择其中80%做训练数据，剩下20%为测试数据，评价指标为RMSE。（10分）\n",
    "4. 用训练数据训练最小二乘线性回归模型（20分）、岭回归模型、Lasso模型，其中岭回归模型（30分）和Lasso模型（30分），注意岭回归模型和Lasso模型的正则超参数调优。\n",
    "5. 比较用上述三种模型得到的各特征的系数，以及各模型在测试集上的性能。并简单说明原因。（10分"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.导入必要的工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import mean_squared_error as mse\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "%matplotlib inline\n",
    "from IPython.display import display, HTML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.探索数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed   cnt  \n",
       "0           2  0.344167  0.363625  0.805833   0.160446   985  \n",
       "1           2  0.363478  0.353739  0.696087   0.248539   801  \n",
       "2           1  0.196364  0.189405  0.437273   0.248309  1349  \n",
       "3           1  0.200000  0.212122  0.590435   0.160296  1562  \n",
       "4           1  0.226957  0.229270  0.436957   0.186900  1600  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dpath = './data/'\n",
    "data = pd.read_csv(\"week4_day.csv\")\n",
    "data = data.drop(['casual','registered'],axis=1) \n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 数据基本信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(731, 14)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "instant       0\n",
       "dteday        0\n",
       "season        0\n",
       "yr            0\n",
       "mnth          0\n",
       "holiday       0\n",
       "weekday       0\n",
       "workingday    0\n",
       "weathersit    0\n",
       "temp          0\n",
       "atemp         0\n",
       "hum           0\n",
       "windspeed     0\n",
       "cnt           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看是否有空值\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "没有缺失数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211.165812</td>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>548.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season          yr        mnth     holiday     weekday  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
       "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
       "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
       "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
       "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
       "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
       "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
       "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
       "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
       "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
       "\n",
       "               cnt  \n",
       "count   731.000000  \n",
       "mean   4504.348837  \n",
       "std    1937.211452  \n",
       "min      22.000000  \n",
       "25%    3152.000000  \n",
       "50%    4548.000000  \n",
       "75%    5956.000000  \n",
       "max    8714.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 各属性的统计特性\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 数值型特征数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(1) 风速"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#风速直方图\n",
    "fig=plt.figure(figsize=(17,5))\n",
    "plt.subplot(121)\n",
    "sns.histplot(data.windspeed.values, bins=30, kde=True)\n",
    "plt.title(\"Histogram of Windspeed\");\n",
    "plt.xlabel('Value of Windspeed', fontsize=12)\n",
    "plt.ylabel('Probability', fontsize=12)\n",
    "\n",
    "#风速散点图\n",
    "plt.subplot(122)\n",
    "plt.scatter(range(data.shape[0]), data[\"windspeed\"].values,color='orange')\n",
    "plt.title(\"Distribution of Windspeed\");\n",
    "plt.xlabel('Count of Windspeeds', fontsize=12)\n",
    "plt.ylabel('Value of Windspeed', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从直方图上可以看到，图像左边比右边陡，不太满足正态分布"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(2) 湿度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#湿度直方图\n",
    "fig=plt.figure(figsize=(17,5))\n",
    "plt.subplot(121)\n",
    "sns.histplot(data.hum.values, bins=30, kde=True)\n",
    "plt.title(\" Histogram of humidity\");\n",
    "plt.xlabel('hum value', fontsize=12)\n",
    "plt.ylabel('Probability', fontsize=12)\n",
    "#湿度散点图\n",
    "plt.subplot(122)\n",
    "plt.scatter(range(data.shape[0]), data[\"hum\"].values,color='orange')\n",
    "plt.title(\"Distribution of humidity\");\n",
    "plt.xlabel('Count of Hum', fontsize=12)\n",
    "plt.ylabel('Value of Hum', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(3) 温度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 14 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   instant     731 non-null    int64  \n",
      " 1   dteday      731 non-null    object \n",
      " 2   season      731 non-null    int64  \n",
      " 3   yr          731 non-null    int64  \n",
      " 4   mnth        731 non-null    int64  \n",
      " 5   holiday     731 non-null    int64  \n",
      " 6   weekday     731 non-null    int64  \n",
      " 7   workingday  731 non-null    int64  \n",
      " 8   weathersit  731 non-null    int64  \n",
      " 9   temp        731 non-null    float64\n",
      " 10  atemp       731 non-null    float64\n",
      " 11  hum         731 non-null    float64\n",
      " 12  windspeed   731 non-null    float64\n",
      " 13  cnt         731 non-null    int64  \n",
      "dtypes: float64(4), int64(9), object(1)\n",
      "memory usage: 80.1+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#温度直方图\n",
    "fig=plt.figure(figsize=(17,5))\n",
    "plt.subplot(121)\n",
    "sns.histplot(data.temp.values, bins=30, kde=True)\n",
    "plt.title(\" Histogram of temperature\");\n",
    "plt.xlabel('temp value', fontsize=12)\n",
    "plt.ylabel('Probability', fontsize=12)\n",
    "#温度散点图\n",
    "plt.subplot(122)\n",
    "plt.scatter(range(data.shape[0]), data[\"temp\"].values,color='orange')\n",
    "plt.title(\"Distribution of temp\");\n",
    "plt.xlabel('Count of Temperature', fontsize=12)\n",
    "plt.ylabel('Value of Temperature', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 类别型特征数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "season属性的不同取值和出现的次数\n",
      "3    188\n",
      "2    184\n",
      "1    181\n",
      "4    178\n",
      "Name: season, dtype: int64\n",
      "\n",
      "mnth属性的不同取值和出现的次数\n",
      "12    62\n",
      "10    62\n",
      "8     62\n",
      "7     62\n",
      "5     62\n",
      "3     62\n",
      "1     62\n",
      "11    60\n",
      "9     60\n",
      "6     60\n",
      "4     60\n",
      "2     57\n",
      "Name: mnth, dtype: int64\n",
      "\n",
      "weathersit属性的不同取值和出现的次数\n",
      "1    463\n",
      "2    247\n",
      "3     21\n",
      "Name: weathersit, dtype: int64\n",
      "\n",
      "weekday属性的不同取值和出现的次数\n",
      "6    105\n",
      "1    105\n",
      "0    105\n",
      "5    104\n",
      "4    104\n",
      "3    104\n",
      "2    104\n",
      "Name: weekday, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#对类别型特征，观察其取值范围及直方图\n",
    "categorical_features = ['season','mnth','weathersit','weekday']\n",
    "for col in categorical_features:\n",
    "    print ('\\n%s属性的不同取值和出现的次数'%col)\n",
    "#     data[col] = data[col].astype('object')\n",
    "    print (data[col].value_counts())\n",
    "    data[col] = data[col].astype('object')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 每年的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23bfaab6320>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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69KhxhYh6pZSi/4BB7M634ZSFfg33fU4QofYQunTpYnQp5yRBImrl4MGDALjCW3Lg4EHcbnnX8RfDhw+n1KnZLb0SQ7nc8E22jcFDhmKxePdQowSJqJUffvgBFWTDGdORkuJi6ZX4kT59+hAeFsrWDKvRpQS0H3KDKHHC6NGjjS7lvCRIxAVzu91s+2o75WGxVES0AmD79u0GVyXqi8ViYfSYsezMtlHikgtKjLI5zUpURDj9+vUzupTzkiARF2zXrl3k5+XiatIWbQ1DhzZl7bp1Rpcl6tHEiRMpr9B8lRFsdCkBqdCp+DbHyphx471+WAskSEQtLF26FGUJxhXVFgBH9EUcPHCA/fv3G1yZqC8XX3wx7dq2YWN6iNGlBKQv06243DB58mSjS6kRCRJxQVJSUli/fj2OmAQwV07GOpsmoCzBvPvuuwZXJ+qLUoqp0y7lx5NmUork4sTGpDVsSAuhc6cELrroIqPLqREJEnFB/v3v19HKTHls958bLcGUNe/Kxo0b+eGHH4wrTtSrcePGEWQxsz5VJt0b048FFlKKTEy7dLrRpdSYIUGilIpSSn2klNqnlEpSSg1SSkUrpT5TSh30fG5S5fiHlFKHlFL7lVITqrT3UUr94PnaS0rJUnMNaf369WzevImy2EvQQadvFlTeshvYwnnq6WcoKyszqEJRn6Kiohg2fASbM0Jk7a1G9MUJKyE2q0+crXWKUT2SfwGrtdYXAz2AJGA2sFZrnQCs9dxHKZUIXAV0BSYCryqlTvW1XwNuAxI8HxMb85sIJMnJyTz9zDO4w5pR3rL7Lw8wB1HSdjCpJ1L4v//7P7SWZcj9waWXXkqJE76SU4EbRZFTsS3Lxthx47HbfWdnx0YPEqVUBDAc+A+A1rpca50PTAfmew6bD1zmuT0dWKS1dmitjwCHgP5KqVggQmu9VVe+ay2o8hhRjzIzM/nTAw/iqICSDqNAVf7aWI9tw3ps20/HVUS2xtG6N2vXruXNN9+UMPEDPXr0oF3bNqxNDUF+nA1vU5oVZ0VlgPsSI3okHYAs4G2l1LdKqbeUUqFAC611GoDnc3PP8a2B41Uen+Jpa+25fWb7LyilblNK7VBK7cjKyqrf78bPnThxgvtmzSIrJ5eii8airT8vZ20qycVUknva8eWxPShv1pn//ve/vPHGG3LFu49TSvGry3/NkQIzPxZ4/2movsytYW2qne7dupKQkGB0ORfEiCCxAL2B17TWvYBiPMNYZ1HdvIc+R/svG7V+Q2vdV2vdt1mzZhdab8DatWsXd9x5J+lZuRQljMcdVoN/O6VwtBtMebOLee+993j00UcpKSlp+GJFgxk/fjz2kBDWHLcZXUqNlLoUNpuNGTNmYLPZKPWRiyq/zQ4is0Rx+a9nGF3KBTMiSFKAFK31V577H1EZLBme4So8nzOrHN+myuPjgFRPe1w17aKOXC4X8+bN47777qPQqSi8eArusObnf+ApSuFoN4iyuH6sX7+eW269lQMHDjRcwaJB2e12Lp0+ne1ZVrJKvf9EzxKXYurUqdxzzz1MmTLFZ67OX3kslJYtmjNs2DCjS7lgjf5bobVOB44rpTp7msYAe4GlwExP20zgU8/tpcBVSimrUiqeykn17Z7hr0Kl1EDP2VrXV3mMqKWkpCRuv/0O5s2bR3l0Bwq7XIq2RV74EymFM7Y7JZ0nkZqdzx133Mnrr79OaWlp/RctGtyMGTMwm8ysOOb9FyjaLZrly5fz8ssvs2LFCuwW75/c2Zdn4eBJM1f+5iqfuJL9TEZV/DvgXaVUMHAYuJHKUPtAKXUzcAy4AkBrvUcp9QGVYeMC7tZanzoZ8U5gHhACrPJ8iFrIzMxk3rx5rFy1ChVsp/SiUbii4+v8vBURsRQmXob1+Ne89957/O+zz7nt1lsYO3YsZrNc6OYrmjZtypSpU1m+7FOmti2laYj3zn2FWDRlRWV8/PHHlfejvDtItIaPj4QS3SSKKVOmGF1OrRgSJFrr74C+1XxpzFmOfwJ4opr2HUC3+q0usOTk5PD+++/zySeLcbndlDdPxNG6F5jrcY0lixVH/FBcTRNwH/+Kp556iv++9x4333QTQ4YMkUDxEddccw0rV67gkyMh3JZYbHQ5fmN3bhD78y3ce+/1WK2+eZq17/WhRL04fvw4ixYtYvWaNVRUVOCM6YijVa/TzsqqbxXhLSjuMg1L3lGSU3fyt7/9jVatW/Pbq69m3LhxPvufKFA0b96cX/96Bu8vWsS4uDLiI+QqxbqqcMN/fwyjVWxLpk6danQ5tSZBEkBcLhfbtm1j8ZIlfLNjB5jMlMckUN6yG9oW0ThFKIUrOp7CJu2w5CWTkv4Dzz33HP9+/XWmTZ3KtGnTaNWqVePUIi7Ytddey+qVK1h4sIK/9M7H5Bvz2F5r7QkbJ4pMPPbg3QQH++5KyxIkASAlJYU1a9awctVqcrKzwBqKo3VvnM06o4MMmjxVJlzR8biatMdcmIYzcx/vLVrEe4sW0bdvXyZNnMjQoUOll+JlwsLCuPPue3jqqaf44oSVMXEOo0vyWdllJj48Ekr//v0YOnSo0eXUiQSJn8rPz2fTpk2sWr2avXv2VPYEIlrh7DgGV1Sbn65ON5xSVES0oiKiFY7yYoKy9rNjVxI7vv6aELudsWPGMHbsWLp3747J5CU1B7jx48fzvzVr+GDXTi6JcdLMiyfevZXWMG9/GJiD+f3v78fXlwmUIPEjRUVFbNmyhbVr17Jjxze43RXokCjK4/rijOmIDvbutXt0cCjlrXtT3qpXZS8l+xDLV65i2bJlNImOZszo0YwePZouXbr4/H88X6aU4o9/+hM333QjbySF81CvkzLEdYHWnrCyKyeIWbPuIjY21uhy6kyCxMfl5+ezZcsWNmzYwDfffENFRQXYwnE0T8QV0wF3SDT42ptulV5KWcUgLPnHceYe5qNPFvPRRx8R07QZI0cMZ/jw4XTr1k3O+jJAbGws9836PU899RRLj4ZwWbxcH1RTx4vMvPdjGAP692f6dN9ZKv5cJEh8UFpaGlu2bGHz5s18//33lYsj2sIpb9oFZ3R73KHNfC88zsYchCumA66YDpS5HFjyj5Gel8zHi5fw8ccfExEZxbChQxg6dCi9e/eWOZVGNH78eL7++msWr/2cTlFOEpu4jC7J65W64OU9kYRHRPHg7Nl+07OWIPEBWmsOHDjAl19+ycZNmzhy+HBlu70J5S0vwdWkPW67D/Y8LpTFiqtpAq6mCZRVOLGcPI4zN5mVaz5jxYoVWK02+vfvx7BhwxgwYACRkbW4Il/UmFKK+++/nwP79/HaXnikbx7RVpkvORutYe6+MDJLTDz/xD+Ijo42uqR6I0HipRwOB999911lz2PLFnJzciqHfMJa4GzTD1dUu8Y7ZdcbmYNwRXfAFd2BMncF5sI0yvOOsXn7TjZt2oRSiq7dujF0yBCGDBlCmzZtzv+c4oLZ7XYeefQx7rzjdl7ZHcHDvfKxyDkR1Vp93MZXmVZuvfUWevbsaXQ59UqCxIvk5+ezbds2tmzZwvbtX+NwlKHMQZRHtMYVn0hFZBt0kG+swNqoTGYqIuOoiIzDoTWmkhws+cf44fBxdv/wb/7973/TOi6O4cOGMXjwYBITE2VepR7Fx8cz+6GH+cc//sE7B0O5obNc9X6mvXkW3v8xlOHDh/Hb3/7W6HLqnQSJwdLS0ti8eTMbNm5kz+7dlfMd1lDKI9rjimpDRUQsmOTHVGNK4Q5tSnloU8pb90Y5irDkH+NY/jHeW/Q+7733HuHhEQwdOoRhw4bRp08fmVepByNHjuSqq65i0aJFxIe7GNFKri85JbvUxJw9kbSJi2P27If8Zl6kKnmHMkBycjIbNmxg/YYNHP7xRwC0PZry2B64otritsf4/3xHI9HWMJwtEnG2SARXOZaTKTjzj7H6s7WsWrUKq83GwAEDGDFiBAMHDvSp7U29zS233MLBAweY/91O2oS56CBLqFBeAS/viaDCbOPxJ5/y298vCZJGkpKSwrp161i37guOHj0CgDusOeVx/XA1CfD5jsZiCf75DLCf5lWS2bj1azZs2EBQcDCDBw1i1KhRDBw4EJtNhhEvhMVi4a9/+xu33XoLr+yBR/vmEhbk3SvvNrR3D4ZypMDM44//xa/n6SRIGlBhYSHr1q1j1erV7EtKAsAd3oLytgNxNWnv9RcI+rUz5lXMRRlYco/8FCq2kBBGjxrFxIkT6d69u18ORzSEqKgoHn3scX53z928kRTG77sXBmzn+sv0YL5ItfHb3/7W55dAOR8JkgaQlJTExx9/zPr1G3C5nGh7Exxx/XDFdEAHhxpdnjiTUlSEt6QivCWOtgMwF6bjzPmRVWs+Y+XKlcTGtuKyy6YzefJkwsPDja7W61188cXcedfdvPTSS3yWYmN8mzKjS2p0GSUm5h0Ip3u3rtx0001Gl9PgJEjqidvtZuPGjby3aBH79+1DWYJxRHfE2TRB5jx8iTL9fFV924FY8o5yIvsgr732GnPnvs2kSRP5zW9+4xfLWjSkX/3qV3y9fTuLtm8jsYmTuLDAmS9xa3g9KQKL1c5f/vo3n9zx8EL5/3fYwLTWbN++ndffeKNy4jwkgrK2A3E2TQBzkNHlibowB/10AaSpOAdn5l6WLF3GsmXLuPTSS7n22muJiYkxukqvpJTigQcf5IaZ1/PWvgr+2jsfc4BcX7LmuI1DJ8385S/306JFC6PLaRQB8qNtGPn5+fz1r3/lwQcf5PCJTErjh1PY9fLKM4QkRPyKOzSGsvhhFHWfQWl0RxYvWcK1117HmjVrKk/ZFr/QpEkT7r1vFocLzHx+IjBOXMgqNfHRkVAGDx7MmDHVbvjqlyRIamnv3r3MvOEGNn+5FUdcXwq7Xo6raUfvWZ69gVmPbcNckoO5JIeQfSuxHttmdEmNQgeH4mg/hKJul1NsieCpp57isccew+WSdaaqM3r0aPr27cPio6EUlPv/8O6iQ3ZMlmBmzZoVUCdo1OhdTyl1X03aAsXBgwf5wx//yMkyN8VdplEeewmYAutKaVNJLqrCiapwYilMx1SSa3RJjUrbIinuPBFH696sW7eORx99rHLlZXEapRS/+929ONwmFh/x77MU9+db+DrLyjXXXEvz5s2NLqdR1fTP55nVtN1Qj3X4DLfbzRNPPElphYmiThMrF0sUgUmZKG/Vk7K4fmzcuIHPP//c6Iq8Urt27Zg8eQrr02xkl/lvj/2TI6E0iYrkyiuvNLqURnfOn6pS6mql1DIgXim1tMrHF0BO45ToXXbu3MnRo0cobdUbbQ0zuhzhBZwtu6FDY1j0/vtGl+K1rrvuOkwmM8uTDdrauYHtz7eQlGfhmmuvC8gLWc931taXQBrQFPhnlfZCYFdDFeXN0tLSACrXwBICQCmcYS1ISz1idCVeq3nz5owdN551/1vFjA4lfnfF++rjNsLDQpk6darRpRjinD0SrXWy1nq91nqQ1npDlY+dWuuAnF2MiooCwFRWYHAlwpuYygqJjJL9T87liiuuwFEBG1L9a5HMrFITO7OsTL/sVwHZG4GaT7ZfrpQ6qJQ6qZQqUEoVKqUC8p20crVYG8Hpuyt3qhEBz1SSi6XgBCOGDze6FK/WoUMHLunejQ3pdr/6r7MhzQpKMW3aNKNLMUxNZ76eBS7VWkdqrSO01uFa64BcZdBut3PzzTdhOXmcoKx9RpcjjFZRjv3oJsLDw7jmmmuMrsbrTZk6jfRixf58/7gW2q1hU7qd/v36BczFh9WpaZBkaK2TGrQSH3L55ZczYMBAbMe2Yck5bHQ5wigVTuyH1mIuzecvf/6zbO1bA8OHD8dmtbIlveGGt9qGuQgxuwkxu7k4yknbsIYbhU/Ks5BXBhMnTWqw1/AFNQ2SHUqp9z1ncV1+6qNBK/NiFouFRx75B926dSPk8HqC0vfIMFeAUc5SQvevIqgog9mzH2TAgAFGl+QTQkJCGD5iBNuzbZQ30GU313YqoV14Be3CK3i4dwHXdippmBcCvky3Yg+xMXjw4AZ7DV9Q0yCJAEqA8cA0z0edTk9QSpmVUt8qpZZ77kcrpT7zzMV8ppRqUuXYh5RSh7J9QhAAAB1WSURBVJRS+5VSE6q091FK/eD52kuqES8ltdls/PO55xg2bBi2419hO7IJ3AF5/kHAMRVlEpa0FKuzgMcff5zx48cbXZJPGT9+PKVO+D4n2OhS6qS8AnZk2xgxclTA77JZ0yAxAb/XWt+otb4RuL8eXvs+oOpw2WxgrdY6AVjruY9SKhG4CugKTAReVUqduoz8NeA2IMHzMbEe6qoxq9XKI488wsyZMwnK/ZGwpOWYSvIaswTRmLQmOG0XoftX0qJJOK/OmRPwf4nWRs+ePWkSFcm2DN8Oku9zgil1EVBrap1NTYPkEq11/qk7Wus8oFdtX1QpFQdMAd6q0jwdmO+5PR+4rEr7Iq21Q2t9BDgE9FdKxQIRWuutunLVvAVVHtNoTCYTN954I888/TSRQW7CkpYSlL4btLuxSxENSDkKsR9YjTVlB8OGDuWtN98kISHB6LJ8ksViYdToMXyXY6XU5bvrUW3NCKZJVCS9etX6rdBv1LhHcsZQUzR1W4L+ReABoOq7bQutdRqA5/OpxWpaA8erHJfiaWvtuX1m+y8opW5TSu1QSu3IysqqQ9lnN2DAAObPe5uBA/pjO76d0P2rUGUnG+S1RCPSmqDMJML3LMFenscDDzzAo488Ihtc1dGYMWNwumFHlm/2Soqdiu9zrIweMxazObDW2atOTYPkn8CXSqnHlFKPUnnF+7O1eUGl1FQgU2v9TU0fUk2bPkf7Lxu1fkNr3Vdr3bdZs2Y1fNkL16RJE5588kkefvhhwtxFhO9ZQnDqd+CWxfx8kak0n9D9K7Elb6VXz0uYP28ekydPDqhVXRtKYmIisS1b8GWGb84t7MgKxumGsWPHGl2KV6hRkGitFwC/BjKALOByrfXCWr7mEOBSpdRRYBEwWin1DpDhGa7C8znTc3wK0KbK4+OAVE97XDXthlJKMX78eBYuWMCwoUOxnthJWNIyzIXpRpcmasrtIvjETkL3fkq4u5gHH3yQfz73HC1btjS6Mr+hlGL8hInszQ0i1+F7Czl+mWGjdatYLr74YqNL8Qo1/glqrfdqrV/RWr+std5b2xfUWj+ktY7TWrenchJ9ndb6WmApP68yPBP41HN7KXCVUsqqlIqnclJ9u2f4q1ApNdBzttb1VR5juJiYGB599BGefPJJmoWase9bifXIJpSz1OjS6kdFOTabjRkzZlQuC1FRbnRF9cJ8MoXwPUuwpn7HmFEjWbhwAZMmTZJeSAMYN24cGtia7lvDW1mlJpLyLEyYKL8Xp3jT5aVPAx8opW4GjgFXAGit9yilPgD2Ai7gbq31qbGiO4F5QAiwyvPhVQYPHkyvXr2YP38+H3zwIdaTxyht1Qdns04+vQmWcpUz9dKp3HPPPWit+WDZGqNLqhPlKMJ2/Cssecm0at2aP9z/d/r06WN0WX4tLi6OromJbErew+S2ZfjKe/KpiynHjRtncCXew9Ag0VqvB9Z7bucA1Z5Hp7V+AniimvYdQLeGq7B+hISEcMcddzBhwgRefPFFvv/+S4KzD1DadiDuMN/cAEdbglm+fDlaa1asWIG2+OimRe4KgtN/wJa+C4vZxA233soVV1xBcLBv/ZXsqyZNnsxzz+3lxwILHSO9/zqsyiVRQujduxexsbIC+Cm++yexD4qPj+fFF1/kr3/9K9HBbkKTlmM9stk3h7vMwZSVlfHxxx9TVlYGZt974zWfTCF87xKsJ3YydPAgFi5YwDXXXCMh0ohGjRqF1RrsMysCJ+VZyCpVTJo02ehSvIoESSNTSjFmzBj+++47/OY3v8GW+yPhez4hKHOfXHvSSJSjiJBDa7Ef+B+tosN49tlneeyxx2Qy3QChoaGMGjWabVk2Sr2/Q8L6VBthoXaGy0rPp5EgMYjdbufOO+9k7tz/0KNrF2zJXxK6bwWm4myjS/NfbjfBabsI37MYe3E6t956K/Pefpv+/fsbXVlAmzZtGg4XbPPyU4ELyxXfZFsZP2FiwC+JciYJEoO1b9+eF154gb/85S9EW1yEJi3DeuwrqHAaXZpf+Wl9rJQdDBk0gIULZRjLWyQmJtKhfXvWp3n3Nryb0q243AT0viNnI0HiBZRSjB07lnfeWcil06YRnLmX8D2LMZ88YXRpvq/CiTV5G6FJy2lqN/Pkk0/y+OOPB/TeEd5GKcWll13GkQIzhwu88ypxt4YvUu1069qV+Ph4o8vxOhIkXiQsLIz777+fOa+8QlzzJtgPrMF6dLP0TmrJXJhO+N5PCc7cy+WXX87CBfNlkUUvNW7cOGzWYNae8M6tavfmBZFRoph+WaMv5+cTJEi8UNeuXfnPW29x9dVXE5x9kPCkTzEVNcwaYX5Juwk+sRP7/lW0iLLzr3/9i3vvvRe73UdPUQ4AoaGhjBs/gW2ZNoqd3ndBydoTViIjwhkxYoTRpXglCRIvZbVauf322/nXiy8SE2oldP/KyjO7xDkpZ2llTy71OyZOmMDbc+fSo0cPo8sSNXDppZfirKici/AmuWUmvs22MnnKVJlTOwsJEi/Xo0cP3p77H/r26Y0t+UusyVtlN8azMJXmE5a0DFtpNrNnz2b27NnSC/EhCQkJJHa5mC9S7V71K74+1YqmMuhE9SRIfEBERATPPP00V155JcGZSdgOr5drTs5gKs4mbP9KIkMsvPzyy0yc2Kh7nIl6Mv2yX5FWrEjK847Vm1xuWJ9up3+//nIl+zlIkPgIs9nMXXfdxe23305Q7hFsR7dIz8TDVJpP2MH/0bRJBK/OmSMrsvqwkSNHEh4W6jWT7t/lBJNfBpdOn250KV5NgsTHXH311ZVb+2YfJChjj9HlGK/CSeiPawm323jh+edp3bravc2Ej7BarUyaPIWd2VZOlhs/6b7uhI1mTaMZMGCA0aV4NQkSH3TDDTcweMgQbCd2YCrNP/8D/Jj1+NdQVsCjj/yDuLi48z9AeL0pU6ZQoWFTmrGT7lmlJnbnBjF5yjQsFu8YavNWEiQ+SCnFA3/6EzarleATNd1o0v+ospMEZ+/nV5ddRs+ePY0uR9STdu3a0b1bVzamGzvpvjHNilIwebIs0Hg+EiQ+Kioqist/9SuC8o/55urB9SAo+yAmpbj22muNLkXUs8lTppJerDhw0piegFvD5gw7ffv2lVUQakCCxIeNGDECtMZcYPgOw4YILkile/dLiImJMboUUc9GjBiBzWpls0HDW0l5FnJKkeXia0iCxIe1b98eAJOj0NhCDGIqLyQ+vr3BVYiGYLfbGTlqFNuzbJRXnP/4+rY53UqoPYQhQ4Y0/ov7IAkSH/bzftHGn91iFNkz23+NHz+eUhd8m924V5M7KuCb7BBGjhoty8XXkASJD0tNrRzScgcH5tXbOsjOiROBOawXCHr06EHTmGi+bOR9SnZmBVPm0owdO7ZRX9eXSZD4sG++qTxjq8JH932vq/LQ5uzatQuHw2F0KaIBmM1mRo8Zy67cYIoacSHHbZlWmsZEyxptF0CCxEdprVm2fAU6tCnaFml0OYZwRXegrKyU9evXG12KaCBjxoyhwg1fZzbO8FaRU7ErN5jRY8ZiMsnbY03Jv5SP2rx5M0ePHKaseRejSzFMRXhLtD2a+QsW4HL5wIbf4oJ16tSJ1q1i+SqzcYa3dmQFU+GuDDBRcxIkPsjhcPDKnDlgb4Ir5iKjyzGOUpS26k3qiRMsXrzY6GpEA1BKMWbsOJLyg8h3NPzw1rYMG61bxdKpU6cGfy1/IkHig+bOnUtGejolbQaCCuwfYUVUG1yRbXjzzbdISUkxuhzRAMaMGYPWNHivJM9Ruerw2HHj5WzACxTY70I+6Ntvv+X9Dz6gvFlnKiJkWWuUoqzdIJxueOLJJ2WIyw+1a9eOjh0vYmtGw64I/FVG5b4jo0ePbtDX8UcSJD4kPz+fRx97HGwRONr0N7ocr6GtYZS0HUTS3r3MmzfP6HJEAxg/fgKHC8ykFTfcW9aWjBA6d0qgXbt2DfYa/kqCxEdorXn66afJy8+nOH4kmIOMLsmruGI6UN40gXfeffen06KF/xgzZgwmpdjcQNvwphSZSS40MX6CbIhWG40eJEqpNkqpL5RSSUqpPUqp+zzt0Uqpz5RSBz2fm1R5zENKqUNKqf1KqQlV2vsopX7wfO0l5ccDm4sXL2bbtm2UxfXDHSprS1XH0XYghETx2OOPk58f2Mvr+5uYmBj69uvLlgw77gZYEXhTuhWz2SRna9WSET0SF/AHrXUXYCBwt1IqEZgNrNVaJwBrPffxfO0qoCswEXhVKWX2PNdrwG1AgufDL/+cSE5O5rXXXsMV1QZnAJ/ue17mIIrjh3PyZAH/fP55o6sR9WzixEnklsGe3Prtjbvc8GVGCIMGDSIqKqpenztQNHqQaK3TtNY7PbcLgSSgNTAdmO85bD5wmef2dGCR1tqhtT4CHAL6K6VigQit9VattQYWVHmM39Ba83/PPYcLE2Xth4L/drrqhdseQ1mrnmzauJHNmzcbXY6oR0OGDCE8LJQN9bwi8Pc5QZx0wOTJU+r1eQOJoXMkSqn2QC/gK6CF1joNKsMGOLXuR2vgeJWHpXjaWntun9le3evcppTaoZTakZWVVZ/fQoPbvHkzu3/4gdLWfdFBIUaX8xO3PRptDkKbg3CFt8Rtjza6pJ+Ut+iOtjfh1ddek7O4/IjVamX8hInszLZSUI/b8K5PDSEmugn9+8sJLLVlWJAopcKAj4FZWuuCcx1aTZs+R/svG7V+Q2vdV2vdt1mzZhderIHeefddCInE2TTB6FJO42g7kAp7DBX2GEovnlw5P+EtTCbKWvUi9cQJ6ZX4malTp+Jyw5Z6mnTPLjOxKyeIyVOmyna6dWBIkCilgqgMkXe11p94mjM8w1V4Pmd62lOANlUeHgeketrjqmn3G6mpqezft4+ypp0C/sLDC+WKaouyhvL552uNLkXUo/j4eLp1TeSLtPrZhndjqhVkO906M+KsLQX8B0jSWledEV0KzPTcngl8WqX9KqWUVSkVT+Wk+nbP8FehUmqg5zmvr/IYv7Bv3z4AKiKqHbET56JMlIe1ZG9SktGViHo2ddqlpBcr9ufXrQdR4YaN6ZXb6cbGysW9dWHEn7lDgOuA0Uqp7zwfk4GngXFKqYPAOM99tNZ7gA+AvcBq4G6t9ak90+4E3qJyAv5HYFWjficN7NQprN40N+JL3EF2Tp6U04D9zciRIwm1h/BFat2udN+VG0RuGUydOq2eKgtcjT4oqLXezNm39Kv2JG6t9RPAE9W07wC61V913iUiIgIA5SyVMKkFk7OEsLBwo8sQ9cxmszFu/ARWLF1CsbOY0KDajXFtSLURFRkh2+nWAxl492KXXHIJAJa8ZIMr8UHuCoILU+nTu5fRlYgGMGXKFJxu2JpRu31KTpYrvssJZuKkyTLJXg8kSLxY8+bN6de/P7bMvShnmdHl+JTgjD3o8lKmTJFrA/xRQkICF3WIZ3N67XrqX6ZbcWuYNGlSPVcWmCRIvNxdd96JSbsIObwetNvocnyCqSgTa+q3DB48hD59+hhdjmggEydN5nCBmRPF5vMffIYvM0Lo3LmTLNBYTyRIvFx8fDz3338/5oJUbEc2SZich6kkh7BDn9OieTMefPABo8sRDWj06NGYlGLbBQ5vnSj2LNA4fsL5DxY1IkHiA6ZMmcJNN91EUM6PhPy4DiqcRpfklcwFaYTtX010ZDj/fO45IiMDcy/7QBETE0PPXj35Kivkgq4p+SojGJNSjBw5ssFqCzQSJD7i+uuv55577iEo/zhh+1agyk4aXZL30Jqg9D3YD6ymdWxzXnn5JeLi4s7/OOHzRo4cRXqxIuUChre+zrbR/ZLuxMTIKtr1RYLEh8yYMYNnn32WMFVO+N6lWLIOUC+X9/ow5Swl5NDn2I5/xaCBA3n93/+Wi8sCyNChQ1FKsSOrZsNbacUmThSZGDFiZMMWFmAkSHxMv379ePvtuVzSLZGQo5sJOfQ5qrzY6LIan9ZYcg4TvmcxtqJ0fve73/HEE08QGhpqdGWiEUVHR5OY2IXvcmq29tZ3OZWBM3jw4IYsK+BIkPig5s2b8/zzz3PXXXcRUpJB+J7FBGUmBUzvRDmKCDm0lpDD60no0I433nidX//615hM8usciAYPHsKRAjP5jvOvCPxdjpX49u1o2bJlI1QWOOR/no8ym81ceeWVzHv7bXp074oteSuh+1ZgKs4xurSG43YTlPYD4Xs+wV6SwR133MGrc+bQoUMHoysTBjq1/Pvu82x4VeaCAyctDBg4qDHKCigSJD6udevWvPD88/z5z38mylxOaNJSrMlbweUwurR6ZS5IIyzpU2wpXzOgX18WLJjPVVddJVclCy666CKiIiPYnXvueZJ9+UFUuCuHh0X9kv+FfkApxbhx4xg4cCBz585lyZIlWPOOUNq6b+U+Jj68q6IqL8Z6fDtBuUdo3rwFs/7ypIxvi9OYTCZ69e7Dd1u/QOuis/66780LIijIQrdufrs8n2GkR+JHwsPDue+++3jjjTdI7NQR29HNhO5bjqnIt3aFBCrXykr7nvDdH2MvTGHmzJksXLhAQkRUq1evXuSVQXrp2d/SkvKtdE3sitVav1v1CumR+KWEhAReeeVlPvvsM1599TXyk5ZR3qwTjri+YKnb0tuNwXzyBPbj26D0JIOGDOF399wjp/SKc+rRowcAB/KDiLX/cli31KU4VmRiZM+ejV1aQJAg8VNKKcaPH8+QIUOYN28eH3/8Mdb8Y5S27oOzaSevHO5S5cVYj31FUN5RYlu1YtY/HmbAgAFGlyV8QNu2bYmMCGd/fhkjWv0ySA6etKA1dO/e3YDq/J8Mbfm50NBQ7r77bt566y26XpyA7eiWyrO7SnKNLu1n2k1Q+h7Cd3+CvegEN910E/PnzZMQETWmlKJrt+4cKqx+2OrQSQsmpUhMTGzkygKDBEmA6NChAy+/9BKzZ88mQpUSuncpwSk7wO0ytC5TSQ5hScuxHf+Kfn16MX/+fK6//nqCg2u3z4QIXF27diW9WFHk/GVv+3CBhfbt2mK32w2ozP/J0FYAUUoxceJEBg0axGuvvcbq1aux5idT0m4IFeGNfIGWu4Lg1G+xpv9AZGQks/7+d0aOHInywiE34Rs6d+4MwNHC09/WtIbDRVaGD+xqRFkBQXokASgyMpLZs2fz3HPP0Tzchn3fSqzHtzda78RUnENY0lKsabuYOGEC7yxcyKhRoyRERJ2cCpIjBacv4JhdZqKoXP/0dVH/JEgCWN++fZk3722mTZtGcPpuwpKWYyrJa7gX1JrgtF2EJi2jiVXxzDPPMHv2bMLDZV91UXfh4eG0bNGc5KLTeySn7nfq1MmIsgKCBEmAs9vt/OEPf+CZZ54hMshNaNJSgjL31fu6XcpZiv3AGqwpOxg+bBgL5stkuqh/CZ06c6z49Pm1Y4VmTEoRHx9vUFX+T4JEADBgwADmvT2XPr17YUv+snI3xnoa6jIVZRKWtBRbaRZ//OMfeeSRfxAREVEvzy1EVR06dCCjWOGu8nfQ8WIzrVrFYrN5/zVUvkqCRPwkOjqaZ595hhtuuIGg3B8J3b8KVV5Sp+e0ZB8idP8qWjQJ59VXX2Xq1KkyFyIaTHx8PBqItrlpG1b5h9CJkmA6XNTR2ML8nASJOI3ZbOaGG27g8ccew1ZeQNi+5ajS6ndjdNujcdujq38irQlO/Y6QIxvpcUl33nzjDRISEhqwciGgXbt2APSMKefaTiU43ZBZon5qFw1DgkRUa+jQobzyysuEW82EH1hZ7QWMjrYDcbQd+MsHa4015WusJ3Yybtw4/vncczKUJRpFXFwcJqVIK6k8cyuz1IxbV175LhqOBIk4q06dOvHqnFdoEm4n7OCas/ZMzhR8YifB6buZPn06Dz30kCz1LhpNUFAQzZs3I8MTJBkllW9xcXFxRpbl9yRIxDm1adOGF194gXBbMGGHPkM5y855fFDWfqxp3zN58mRmzZoluxaKRhfXpg0ZZZV/vGSWVgZKq1atjCzJ7/n8/3Kl1ESl1H6l1CGl1Gyj6/FHbdu25amnnsTiKiXk8PqznhpsKsrElryVvv36cf/998ukujBEbGwrchyVQZJVZsIeYpOh1Qbm00GilDIDc4BJQCJwtVJKVmVrAF27dmXWrPswF6QSlLH7lwdUOAk9spFmzZry97/9TYazhGFatmxJgUPjqIDsMjMtWrSQP2oamE8HCdAfOKS1Pqy1LgcWAdMNrslvTZkyhUGDBhGS+i2qvPi0rwWn7YKyAv788MNypbowVLNmzQDILTOR67DQvEUjryMXgHw9SFoDx6vcT/G0nUYpdZtSaodSakdWlg/uFugllFLce++9mJUi+MS3P7c7S7Bm7GbMmDH0lI2DhMF+ChKHibxyM82bNze4Iv/n60FSXX/1FwP4Wus3tNZ9tdZ9T/2SidqJjY1l6tQpBOce+ulixaD0vSjt5sYbbzS4OiGgadOmQOWwVoFDExMTY3BF/s/XgyQFaFPlfhyQalAtAWPGjBngdhOUcxC0G1vuIQYNGiSnWAqvcCo4jhWZT7svGo6vB8nXQIJSKl4pFQxcBSw1uCa/FxcXx8VduhCUl4y5MANdXsKECROMLksIAEJCQggOCiLFs+pvkyZNDK7I//l0kGitXcA9wBogCfhAa73H2KoCw5DBgzEVZ2PJPYzJbKZfv35GlyQEUDmX1yQqkpTiyh6JBEnD8/lzNLXWK4GVRtcRaLp37w5AcNZ+LurUSbYwFV4lMiqKjKzsytuRkQZX4/98ukcijNOx48+rqXaWDYOEl4mM+rkXIkHS8CRIRK2EhYURFBQEyDpGwvucupZJKUVoaKjB1fg/CRJRa6eWhW/fvr2xhQhxhlNBEmYPkfXeGoHPz5EI4zz33HPk5ORIj0R4nVO9EOmNNA4JElFrdrtdJtmFVzoVIBbP8KtoWNLnE0L4nVN/4Cglb3GNQf6VhRB+JyQkBKhmvSTRICRIhBB+x2azAcjy8Y1EgkQI4XesVisA+iybsIn6JUEihPA7p4JEeiSNQ4JECOF3Tl0sKz2SxiFBIoTwO6eCRHokjUOCRAjhdywWuUSuMUmQCCH8jgRJ45IgEUL4nYiICABGjx5tcCWBQWJbCOF3oqOj+eSTT4iKijK6lIAgQSKE8EvR0dFGlxAwZGhLCCFEnUiQCCGEqBMJEiGEEHUiQSKEEKJOJEiEEELUiQSJEEKIOpEgEUIIUScq0FbHVEplAclG1+FHmgLZRhchRDXkd7N+tdNaN6vuCwEXJKJ+KaV2aK37Gl2HEGeS383GI0NbQggh6kSCRAghRJ1IkIi6esPoAoQ4C/ndbCQyRyKEEKJOpEcihBCiTiRIhBBC1IkEiagVpdREpdR+pdQhpdRso+sR4hSl1FylVKZSarfRtQQKCRJxwZRSZmAOMAlIBK5WSiUaW5UQP5kHTDS6iEAiQSJqoz9wSGt9WGtdDiwCphtckxAAaK03ArlG1xFIJEhEbbQGjle5n+JpE0IEIAkSURuqmjY5j1yIACVBImojBWhT5X4ckGpQLUIIg0mQiNr4GkhQSsUrpYKBq4ClBtckhDCIBIm4YFprF3APsAZIAj7QWu8xtiohKiml3gO2Ap2VUilKqZuNrsnfyRIpQggh6kR6JEIIIepEgkQIIUSdSJAIIYSoEwkSIYQQdSJBIoQQok4kSIQQQtSJBIkQXsqzyrIQXs9idAFCBCql1GNAttb6X577TwAZwK+ANKAnlcv0C+HV5IJEIQyilGoPfKK17q2UMgEHgQeABUA3rfURA8sTosakRyKEQbTWR5VSOUqpXkAL4FsgB9guISJ8iQSJEMZ6C7gBaAnM9bQVG1aNELUgk+1CGGsxldvC9qNyEUwhfI70SIQwkNa6XCn1BZCvta5Qqro9w4TwbjLZLoSBPJPsO4ErtNYHja5HiNqQoS0hDKKUSgQOAWslRIQvkx6JEEKIOpEeiRBCiDqRIBFCCFEnEiRCCCHqRIJECCFEnUiQCCGEqJP/B6RaHNNAUn52AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(data=data[['yr','cnt']],x=\"yr\",y=\"cnt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2011年和2012年的分布差异很大"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 一年中每天的骑车量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'dayly distribution of counts')]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import datetime\n",
    "\n",
    "\n",
    "data['date'] = pd.to_datetime(data['dteday'])\n",
    "data['dayofyear'] = data[\"date\"].dt.dayofyear  #减今年的第几天\n",
    "fig,ax = plt.subplots()\n",
    "sns.pointplot(\n",
    "             x='dayofyear',y='cnt',\n",
    "    data=data[['dayofyear',\n",
    "                           'cnt',\n",
    "                           'yr']],\n",
    "             hue='yr',ax=ax)\n",
    "ax.set(title=\"dayly distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "相邻天的骑车数目不是特别连续，可以不考虑增加时序特征（y(t-1) - y(t-2)...）每年开始和结束的数量少，中间的多，骑行量应该和月份和季节有关"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 季节与骑车数量的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Seasonly distribution of counts')]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "sns.barplot(data=data[['season',\n",
    "                       'cnt']],\n",
    "           x=\"season\",y=\"cnt\")\n",
    "ax.set(title=\"Seasonly distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出，季节对骑车数量有一定影响"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 月份与骑车数量的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Monthly distribution of counts')]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "sns.barplot(data=data[['mnth',\n",
    "                       'cnt']],\n",
    "           x=\"mnth\",y=\"cnt\")\n",
    "ax.set(title=\"Monthly distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从表中可以看出 5月- 10月 是一年中骑车数量最多的几个月份"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 天气和骑车数目的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'weathersit distribution of counts')]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "sns.barplot(data=data[['weathersit',\n",
    "                       'cnt']],\n",
    "           x=\"weathersit\",y=\"cnt\")\n",
    "ax.set(title=\"weathersit distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出天气对骑车数量有影响"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 一周中的哪天和骑车数目的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'weekday distribution of counts')]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "sns.barplot(data=data[['weekday',\n",
    "                       'cnt']],\n",
    "           x=\"weekday\",y=\"cnt\")\n",
    "ax.set(title=\"weekday distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 工作日和节假日的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23bffa830b8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fig,(ax1,ax2) = plt.subplots(ncols=2)\n",
    "# sns.barplot(data=data,x='holiday',y='cnt',hue='season',ax=ax1)\n",
    "# sns.barplot(data=data,x='workingday',y='cnt',hue='season',ax=ax2)\n",
    "\n",
    "fig=plt.figure(figsize=(17,5))\n",
    "plt.subplot(121)\n",
    "sns.barplot(data=data,x='holiday',y='cnt',hue='season')\n",
    "plt.subplot(122)\n",
    "sns.barplot(data=data,x='workingday',y='cnt',hue='season')\n",
    "\n",
    "\n",
    "# fig,(ax) = plt.subplots()\n",
    "# sns.barplot(data=data[['holiday','cnt']],x='holiday',y='cnt')\n",
    "# # ax.set(title=\"holiday distribution of counts\")\n",
    "\n",
    "# fig,(ax) = plt.subplots()\n",
    "# sns.barplot(data=data[['workingday','cnt']],x='workingday',y='cnt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23bd75014e0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrMatt = data[[\"temp\",\"atemp\",\n",
    "                  \"hum\",\"windspeed\",\n",
    "#                   \"casual\",\"registered\",\n",
    "                  \"cnt\"]].corr()\n",
    "mask = np.array(corrMatt)\n",
    "mask[np.tril_indices_from(mask)] = False\n",
    "sns.heatmap(corrMatt, mask=mask,\n",
    "           vmax=.8, square=True,annot=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "体感温度和温度高度相关\n",
    "目标cnt与温度正相关、湿度和风速负相关"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 特征工程 数据预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据准备"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将 dteday 这列非数值类型转换成数值类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant  dteday season  yr mnth  holiday weekday  workingday weathersit  \\\n",
       "0        1       1      1   0    1        0       6           0          2   \n",
       "1        2       2      1   0    1        0       0           0          2   \n",
       "2        3       3      1   0    1        0       1           1          1   \n",
       "3        4       4      1   0    1        0       2           1          1   \n",
       "4        5       5      1   0    1        0       3           1          1   \n",
       "\n",
       "       temp     atemp       hum  windspeed   cnt  \n",
       "0  0.344167  0.363625  0.805833   0.160446   985  \n",
       "1  0.363478  0.353739  0.696087   0.248539   801  \n",
       "2  0.196364  0.189405  0.437273   0.248309  1349  \n",
       "3  0.200000  0.212122  0.590435   0.160296  1562  \n",
       "4  0.226957  0.229270  0.436957   0.186900  1600  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import datetime\n",
    "\n",
    "data['date'] = pd.to_datetime(data['dteday'])\n",
    "data['dteday'] = data[\"date\"].dt.dayofyear  #将日期转换成一年的第多少天。比如2011-01-01转换成第1天。\n",
    "data = data.drop(['date','dayofyear'],axis =1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 离散型特征进行预处理\n",
    "categorical_features = ['season','mnth','weathersit','weekday']\n",
    "x_train_cat = data[categorical_features]\n",
    "x_train_cat = pd.get_dummies(x_train_cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(731, 10)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop(['season','mnth','weathersit','weekday'],axis =1)\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(731, 36)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# FE_train = pd.concat([data['instant'],new_data,data['yr'],data['cnt']], axis = 1)\n",
    "data =  pd.concat([data,x_train_cat], axis = 1)\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.355170</td>\n",
       "      <td>0.373517</td>\n",
       "      <td>0.828620</td>\n",
       "      <td>0.284606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.379232</td>\n",
       "      <td>0.360541</td>\n",
       "      <td>0.715771</td>\n",
       "      <td>0.466215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.171000</td>\n",
       "      <td>0.144830</td>\n",
       "      <td>0.449638</td>\n",
       "      <td>0.465740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.175530</td>\n",
       "      <td>0.174649</td>\n",
       "      <td>0.607131</td>\n",
       "      <td>0.284297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.209120</td>\n",
       "      <td>0.197158</td>\n",
       "      <td>0.449313</td>\n",
       "      <td>0.339143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       temp     atemp       hum  windspeed\n",
       "0  0.355170  0.373517  0.828620   0.284606\n",
       "1  0.379232  0.360541  0.715771   0.466215\n",
       "2  0.171000  0.144830  0.449638   0.465740\n",
       "3  0.175530  0.174649  0.607131   0.284297\n",
       "4  0.209120  0.197158  0.449313   0.339143"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ++++\n",
    "numerical_features = ['temp','atemp','hum','windspeed']\n",
    "#数值型变量预处理\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "mn_x = MinMaxScaler()\n",
    "\n",
    "temp = mn_x.fit_transform(data[numerical_features])\n",
    "\n",
    "data_num = pd.DataFrame(data=temp, columns=numerical_features, index =data.index)\n",
    "data_num.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop(numerical_features,axis = 1)\n",
    "data = pd.concat([data,data_num], axis = 1, ignore_index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 从原始数据中分离输入特征x 和输出 y\n",
    "y = data['cnt'].values\n",
    "X = data.drop('cnt',axis = 1)\n",
    "#用于后续显示权重系数对应的特征\n",
    "columns = X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lee\\Miniconda3\\envs\\tf2\\lib\\site-packages\\pandas\\core\\frame.py:3997: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  errors=errors,\n"
     ]
    }
   ],
   "source": [
    "# 将数据分割训练数据和测试数据\n",
    "# from sklearn.cross_validation import train_test_split\n",
    "from sklearn.model_selection import train_test_split\n",
    "# 随机采样 20% 的数据构建测试数据，80%作为训练数据\n",
    "X_train,X_test, y_train, y_test = train_test_split(X,   y,  test_size = 0.2,    random_state = 0)\n",
    "\n",
    "#保存测试ID，用于结果提交\n",
    "testID = X_test['instant']\n",
    "X_train.drop('instant', axis=1, inplace = True)\n",
    "# data.drop('cnt',axis = 1)\n",
    "X_test.drop('instant', axis=1, inplace = True)\n",
    "\n",
    "# 保存特征名字以备后用（可视化）\n",
    "feat_names = X_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 分别初始化对特征和目标值的标准化器\n",
    "ss_X = StandardScaler()\n",
    "ss_y = StandardScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征以及目标值进行标准化处理\n",
    "X_train = ss_X.fit_transform(X_train)\n",
    "X_test = ss_X.transform(X_test)\n",
    "\n",
    "#对y做标准化不是必须\n",
    "#对y标准化的好处是不同问题的w差异不太大，同时正则参数的范围也有限\n",
    "y_train = ss_y.fit_transform(y_train.reshape(-1, 1))\n",
    "y_test = ss_y.transform(y_test.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.确定模型类型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1 尝试缺省参数的线性回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE on Training set : 0.39431674181128923\n",
      "RMSE on Test set : 0.4116240858355063\n",
      "r2_train: 0.8445143071273291\n",
      "r2_test: 0.8557880730351773\n"
     ]
    }
   ],
   "source": [
    "# 线性回归\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# 使用默认配置初始化\n",
    "lr = LinearRegression()\n",
    "\n",
    "# 训练模型参数\n",
    "lr.fit(X_train,y_train)\n",
    "\n",
    "#预测\n",
    "y_test_pred_lr = lr.predict(X_test)\n",
    "y_train_pred_lr = lr.predict(X_train)\n",
    "\n",
    "\n",
    "\n",
    "rmse_train = np.sqrt(mse(y_train,y_train_pred_lr))\n",
    "rmse_test = np.sqrt(mse(y_test,y_test_pred_lr))\n",
    "# mse_train = mse(y_train,y_train_pred_lr)\n",
    "# print(\"MSE on Training set:\",mse_train)\n",
    "print(\"RMSE on Training set :\", rmse_train)\n",
    "print(\"RMSE on Test set :\", rmse_test)\n",
    "\n",
    "\n",
    "r2_score_train = r2_score(y_train,y_train_pred_lr)\n",
    "r2_score_test = r2_score(y_test,y_test_pred_lr)\n",
    "\n",
    "print(\"r2_train:\",r2_score_train)\n",
    "print(\"r2_test:\",r2_score_test)\n",
    "\n",
    "# # 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "# fs = pd.DataFrame({\"columns\":list(columns), \"coef\":list((lr.coef_.T))})\n",
    "# fs.sort_values(by=['coef'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 模型评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>coef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>yr</td>\n",
       "      <td>[0.5120802216168161]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>temp</td>\n",
       "      <td>[0.3509956339033794]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>season_4</td>\n",
       "      <td>[0.15239503338341406]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>mnth_9</td>\n",
       "      <td>[0.15163537217531473]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>mnth_10</td>\n",
       "      <td>[0.13840354167606672]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>mnth_12</td>\n",
       "      <td>[0.11560578072768136]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>atemp</td>\n",
       "      <td>[0.11147710322898721]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>mnth_11</td>\n",
       "      <td>[0.07538378033548292]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>weathersit_1</td>\n",
       "      <td>[0.06816989057002558]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>mnth_8</td>\n",
       "      <td>[0.04607586056694693]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>weekday_6</td>\n",
       "      <td>[0.03155236553254804]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>weekday_5</td>\n",
       "      <td>[0.018005349806666703]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>season_3</td>\n",
       "      <td>[0.01718282238980799]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>workingday</td>\n",
       "      <td>[0.016168900910427116]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>mnth_5</td>\n",
       "      <td>[0.01606616957287441]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>weekday_4</td>\n",
       "      <td>[0.014810860819678362]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>season_2</td>\n",
       "      <td>[0.012164414064620257]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>weekday_3</td>\n",
       "      <td>[0.002628140810272862]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>weekday_2</td>\n",
       "      <td>[-0.005042303246017356]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>mnth_6</td>\n",
       "      <td>[-0.016965310919035533]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>weekday_1</td>\n",
       "      <td>[-0.022032841518754037]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>weathersit_2</td>\n",
       "      <td>[-0.02622771508275496]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>holiday</td>\n",
       "      <td>[-0.02740068565607052]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>weekday_0</td>\n",
       "      <td>[-0.040168280214920166]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>mnth_7</td>\n",
       "      <td>[-0.05286780304627258]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>mnth_3</td>\n",
       "      <td>[-0.0685000798268201]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>mnth_4</td>\n",
       "      <td>[-0.07023248098438316]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>[-0.1240675310920183]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>weathersit_3</td>\n",
       "      <td>[-0.12871835634088702]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>hum</td>\n",
       "      <td>[-0.13817231583763126]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>mnth_2</td>\n",
       "      <td>[-0.146271269781228]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>season_1</td>\n",
       "      <td>[-0.18450204625098793]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>mnth_1</td>\n",
       "      <td>[-0.19501544643340768]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dteday</td>\n",
       "      <td>[-0.3607766672373165]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         columns                     coef\n",
       "1             yr     [0.5120802216168161]\n",
       "30          temp     [0.3509956339033794]\n",
       "7       season_4    [0.15239503338341406]\n",
       "16        mnth_9    [0.15163537217531473]\n",
       "17       mnth_10    [0.13840354167606672]\n",
       "19       mnth_12    [0.11560578072768136]\n",
       "31         atemp    [0.11147710322898721]\n",
       "18       mnth_11    [0.07538378033548292]\n",
       "20  weathersit_1    [0.06816989057002558]\n",
       "15        mnth_8    [0.04607586056694693]\n",
       "29     weekday_6    [0.03155236553254804]\n",
       "28     weekday_5   [0.018005349806666703]\n",
       "6       season_3    [0.01718282238980799]\n",
       "3     workingday   [0.016168900910427116]\n",
       "12        mnth_5    [0.01606616957287441]\n",
       "27     weekday_4   [0.014810860819678362]\n",
       "5       season_2   [0.012164414064620257]\n",
       "26     weekday_3   [0.002628140810272862]\n",
       "25     weekday_2  [-0.005042303246017356]\n",
       "13        mnth_6  [-0.016965310919035533]\n",
       "24     weekday_1  [-0.022032841518754037]\n",
       "21  weathersit_2   [-0.02622771508275496]\n",
       "2        holiday   [-0.02740068565607052]\n",
       "23     weekday_0  [-0.040168280214920166]\n",
       "14        mnth_7   [-0.05286780304627258]\n",
       "10        mnth_3    [-0.0685000798268201]\n",
       "11        mnth_4   [-0.07023248098438316]\n",
       "33     windspeed    [-0.1240675310920183]\n",
       "22  weathersit_3   [-0.12871835634088702]\n",
       "32           hum   [-0.13817231583763126]\n",
       "9         mnth_2     [-0.146271269781228]\n",
       "4       season_1   [-0.18450204625098793]\n",
       "8         mnth_1   [-0.19501544643340768]\n",
       "0         dteday    [-0.3607766672373165]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(feat_names), \"coef\":list((lr.coef_.T))})\n",
    "fs.sort_values(by=['coef'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 正则化的线性回归（L2正则 --> 岭回归）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 模型评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#岭回归／L2正则\n",
    "#class sklearn.linear_model.RidgeCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True, \n",
    "#                                  normalize=False, scoring=None, cv=None, gcv_mode=None, \n",
    "#                                  store_cv_values=False)\n",
    "from sklearn.linear_model import  RidgeCV\n",
    "\n",
    "#设置超参数（正则参数）范围\n",
    "alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "#n_alphas = 20\n",
    "#alphas = np.logspace(-5,2,n_alphas)\n",
    "\n",
    "#生成一个RidgeCV实例\n",
    "# ridge = RidgeCV(alphas=alphas, store_cv_values=True)  \n",
    "ridge = RidgeCV(alphas=alphas, cv = 5)  \n",
    "\n",
    "#模型训练\n",
    "ridge.fit(X_train, y_train)    \n",
    "\n",
    "#预测\n",
    "y_test_pred_ridge = ridge.predict(X_test)\n",
    "y_train_pred_ridge = ridge.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rmse on train set: 0.39538638279502286\n",
      "rmse on test set: 0.41308905807643925\n"
     ]
    }
   ],
   "source": [
    "# 评估，使用rmse评价模型在测试集和训练集上的性能\n",
    "rmse_train = np.sqrt(mse(y_train,y_train_pred_ridge))\n",
    "rmse_test = np.sqrt(mse(y_test,y_test_pred_ridge))\n",
    "print('rmse on train set:',rmse_train)\n",
    "print('rmse on test set:',rmse_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is: 10.0\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>coef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>yr</td>\n",
       "      <td>[0.503631314007401]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>temp</td>\n",
       "      <td>[0.2511189971324577]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>atemp</td>\n",
       "      <td>[0.18975529513273853]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>season_4</td>\n",
       "      <td>[0.14683173021639442]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>mnth_9</td>\n",
       "      <td>[0.10126801182431759]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>weathersit_1</td>\n",
       "      <td>[0.06701091469396332]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>mnth_5</td>\n",
       "      <td>[0.05641011697633028]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>mnth_10</td>\n",
       "      <td>[0.054277410313525246]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>weekday_6</td>\n",
       "      <td>[0.030782644158985278]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>mnth_8</td>\n",
       "      <td>[0.02003899475447709]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>weekday_5</td>\n",
       "      <td>[0.019318297275929493]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>workingday</td>\n",
       "      <td>[0.015982578925309198]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>season_3</td>\n",
       "      <td>[0.015536941977268865]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>weekday_4</td>\n",
       "      <td>[0.014855344577688482]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>season_2</td>\n",
       "      <td>[0.012933827866272445]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>mnth_3</td>\n",
       "      <td>[0.0126897051510936]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>mnth_6</td>\n",
       "      <td>[0.0032741290733800444]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>weekday_3</td>\n",
       "      <td>[0.0029512656197777667]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>weekday_2</td>\n",
       "      <td>[-0.004909335236438471]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>mnth_4</td>\n",
       "      <td>[-0.011317007299342822]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>weekday_1</td>\n",
       "      <td>[-0.023409361001580416]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>mnth_12</td>\n",
       "      <td>[-0.02363935873138581]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>weathersit_2</td>\n",
       "      <td>[-0.025359076901812896]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>holiday</td>\n",
       "      <td>[-0.025884476719685145]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>mnth_11</td>\n",
       "      <td>[-0.03526650329981513]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>weekday_0</td>\n",
       "      <td>[-0.03986332955763131]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>mnth_7</td>\n",
       "      <td>[-0.05171019681143839]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>mnth_2</td>\n",
       "      <td>[-0.052073684635153675]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dteday</td>\n",
       "      <td>[-0.05881451589172962]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>mnth_1</td>\n",
       "      <td>[-0.07457439595345973]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>[-0.12076456088391704]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>weathersit_3</td>\n",
       "      <td>[-0.12779183103536268]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>hum</td>\n",
       "      <td>[-0.1365408991663952]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>season_1</td>\n",
       "      <td>[-0.17798071622643852]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         columns                     coef\n",
       "1             yr      [0.503631314007401]\n",
       "30          temp     [0.2511189971324577]\n",
       "31         atemp    [0.18975529513273853]\n",
       "7       season_4    [0.14683173021639442]\n",
       "16        mnth_9    [0.10126801182431759]\n",
       "20  weathersit_1    [0.06701091469396332]\n",
       "12        mnth_5    [0.05641011697633028]\n",
       "17       mnth_10   [0.054277410313525246]\n",
       "29     weekday_6   [0.030782644158985278]\n",
       "15        mnth_8    [0.02003899475447709]\n",
       "28     weekday_5   [0.019318297275929493]\n",
       "3     workingday   [0.015982578925309198]\n",
       "6       season_3   [0.015536941977268865]\n",
       "27     weekday_4   [0.014855344577688482]\n",
       "5       season_2   [0.012933827866272445]\n",
       "10        mnth_3     [0.0126897051510936]\n",
       "13        mnth_6  [0.0032741290733800444]\n",
       "26     weekday_3  [0.0029512656197777667]\n",
       "25     weekday_2  [-0.004909335236438471]\n",
       "11        mnth_4  [-0.011317007299342822]\n",
       "24     weekday_1  [-0.023409361001580416]\n",
       "19       mnth_12   [-0.02363935873138581]\n",
       "21  weathersit_2  [-0.025359076901812896]\n",
       "2        holiday  [-0.025884476719685145]\n",
       "18       mnth_11   [-0.03526650329981513]\n",
       "23     weekday_0   [-0.03986332955763131]\n",
       "14        mnth_7   [-0.05171019681143839]\n",
       "9         mnth_2  [-0.052073684635153675]\n",
       "0         dteday   [-0.05881451589172962]\n",
       "8         mnth_1   [-0.07457439595345973]\n",
       "33     windspeed   [-0.12076456088391704]\n",
       "22  weathersit_3   [-0.12779183103536268]\n",
       "32           hum    [-0.1365408991663952]\n",
       "4       season_1   [-0.17798071622643852]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print ('alpha is:', ridge.alpha_)\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(feat_names), \"coef\":list((ridge.coef_.T))})\n",
    "fs.sort_values(by=['coef'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "相比OLS，岭回归模型增加了L2正则，系数值进行了收缩。\n",
    "由于增加正则限制了模型复杂的，相比比OLS模型，岭回归模型在训练集上和测试集上的误差有所减小。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 正则化的线性回归（L1正则 --> Lasso）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best alpha : 10.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lee\\Miniconda3\\envs\\tf2\\lib\\site-packages\\sklearn\\utils\\validation.py:73: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n"
     ]
    }
   ],
   "source": [
    "#### Lasso／L1正则\n",
    "# class sklearn.linear_model.LassoCV(eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, \n",
    "#                                    normalize=False, precompute=’auto’, max_iter=1000, \n",
    "#                                    tol=0.0001, copy_X=True, cv=None, verbose=False, n_jobs=1,\n",
    "#                                    positive=False, random_state=None, selection=’cyclic’)\n",
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "#设置超参数搜索范围\n",
    "alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "\n",
    "#生成一个LassoCV实例\n",
    "lasso = LassoCV(cv=5)  \n",
    "# lasso = LassoCV(alphas=alphas,cv = 5)  \n",
    "\n",
    "#训练（内含CV）\n",
    "lasso.fit(X_train, y_train)  \n",
    "\n",
    "#通过交叉验证得到的最佳超参数alpha\n",
    "alpha = ridge.alpha_\n",
    "print(\"Best alpha :\", alpha)\n",
    "\n",
    "#测试\n",
    "y_test_pred_lasso = lasso.predict(X_test)\n",
    "y_train_pred_lasso = lasso.predict(X_train)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 模型评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rmse on train set: 0.3962078405989655\n",
      "rmse on test set: 0.4160254606411835\n"
     ]
    }
   ],
   "source": [
    "# 评估，使用RMSE评价模型在测试集和训练集上的性能\n",
    "# 评估，使用rmse评价模型在测试集和训练集上的性能\n",
    "rmse_train = np.sqrt(mse(y_train,y_train_pred_lasso))\n",
    "rmse_test = np.sqrt(mse(y_test,y_test_pred_lasso))\n",
    "print('rmse on train set:',rmse_train)\n",
    "print('rmse on test set:',rmse_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>coef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>yr</td>\n",
       "      <td>0.506015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>temp</td>\n",
       "      <td>0.349194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>atemp</td>\n",
       "      <td>0.114613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>season_4</td>\n",
       "      <td>0.096102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>mnth_9</td>\n",
       "      <td>0.088945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>weathersit_1</td>\n",
       "      <td>0.087980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>mnth_10</td>\n",
       "      <td>0.058906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>mnth_5</td>\n",
       "      <td>0.056655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>mnth_3</td>\n",
       "      <td>0.040466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>weekday_6</td>\n",
       "      <td>0.013862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>weekday_5</td>\n",
       "      <td>0.012709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>weekday_4</td>\n",
       "      <td>0.007336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>mnth_8</td>\n",
       "      <td>0.001111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>weekday_3</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>weathersit_2</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dteday</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>workingday</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>mnth_4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>mnth_6</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>season_3</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>season_2</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>weekday_2</td>\n",
       "      <td>-0.003628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>mnth_2</td>\n",
       "      <td>-0.008009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>mnth_12</td>\n",
       "      <td>-0.018054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>mnth_1</td>\n",
       "      <td>-0.022242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>weekday_1</td>\n",
       "      <td>-0.022948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>mnth_11</td>\n",
       "      <td>-0.023747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>holiday</td>\n",
       "      <td>-0.029918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>weekday_0</td>\n",
       "      <td>-0.049638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>mnth_7</td>\n",
       "      <td>-0.058764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>weathersit_3</td>\n",
       "      <td>-0.120234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>-0.120262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>hum</td>\n",
       "      <td>-0.135913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>season_1</td>\n",
       "      <td>-0.216443</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         columns      coef\n",
       "1             yr  0.506015\n",
       "30          temp  0.349194\n",
       "31         atemp  0.114613\n",
       "7       season_4  0.096102\n",
       "16        mnth_9  0.088945\n",
       "20  weathersit_1  0.087980\n",
       "17       mnth_10  0.058906\n",
       "12        mnth_5  0.056655\n",
       "10        mnth_3  0.040466\n",
       "29     weekday_6  0.013862\n",
       "28     weekday_5  0.012709\n",
       "27     weekday_4  0.007336\n",
       "15        mnth_8  0.001111\n",
       "26     weekday_3  0.000000\n",
       "21  weathersit_2 -0.000000\n",
       "0         dteday -0.000000\n",
       "3     workingday  0.000000\n",
       "11        mnth_4  0.000000\n",
       "13        mnth_6 -0.000000\n",
       "6       season_3 -0.000000\n",
       "5       season_2  0.000000\n",
       "25     weekday_2 -0.003628\n",
       "9         mnth_2 -0.008009\n",
       "19       mnth_12 -0.018054\n",
       "8         mnth_1 -0.022242\n",
       "24     weekday_1 -0.022948\n",
       "18       mnth_11 -0.023747\n",
       "2        holiday -0.029918\n",
       "23     weekday_0 -0.049638\n",
       "14        mnth_7 -0.058764\n",
       "22  weathersit_3 -0.120234\n",
       "33     windspeed -0.120262\n",
       "32           hum -0.135913\n",
       "4       season_1 -0.216443"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(feat_names), \"coef\":list((lasso.coef_.T))})\n",
    "fs.sort_values(by=['coef'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lasso模型增加了L1正则，系数值进行了收缩，同时有些特征的系数为0。\n",
    "在这个例子中，岭回归模型比Lasso模型性能稍好。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>lr_coef</th>\n",
       "      <th>ridge_coef</th>\n",
       "      <th>lasso_coef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dteday</td>\n",
       "      <td>[-0.3607766672373165]</td>\n",
       "      <td>[-0.05881451589172962]</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>yr</td>\n",
       "      <td>[0.5120802216168161]</td>\n",
       "      <td>[0.503631314007401]</td>\n",
       "      <td>0.506015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>holiday</td>\n",
       "      <td>[-0.02740068565607052]</td>\n",
       "      <td>[-0.025884476719685145]</td>\n",
       "      <td>-0.029918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>workingday</td>\n",
       "      <td>[0.016168900910427116]</td>\n",
       "      <td>[0.015982578925309198]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>season_1</td>\n",
       "      <td>[-0.18450204625098793]</td>\n",
       "      <td>[-0.17798071622643852]</td>\n",
       "      <td>-0.216443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>season_2</td>\n",
       "      <td>[0.012164414064620257]</td>\n",
       "      <td>[0.012933827866272445]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>season_3</td>\n",
       "      <td>[0.01718282238980799]</td>\n",
       "      <td>[0.015536941977268865]</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>season_4</td>\n",
       "      <td>[0.15239503338341406]</td>\n",
       "      <td>[0.14683173021639442]</td>\n",
       "      <td>0.096102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>mnth_1</td>\n",
       "      <td>[-0.19501544643340768]</td>\n",
       "      <td>[-0.07457439595345973]</td>\n",
       "      <td>-0.022242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>mnth_2</td>\n",
       "      <td>[-0.146271269781228]</td>\n",
       "      <td>[-0.052073684635153675]</td>\n",
       "      <td>-0.008009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>mnth_3</td>\n",
       "      <td>[-0.0685000798268201]</td>\n",
       "      <td>[0.0126897051510936]</td>\n",
       "      <td>0.040466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>mnth_4</td>\n",
       "      <td>[-0.07023248098438316]</td>\n",
       "      <td>[-0.011317007299342822]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>mnth_5</td>\n",
       "      <td>[0.01606616957287441]</td>\n",
       "      <td>[0.05641011697633028]</td>\n",
       "      <td>0.056655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>mnth_6</td>\n",
       "      <td>[-0.016965310919035533]</td>\n",
       "      <td>[0.0032741290733800444]</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>mnth_7</td>\n",
       "      <td>[-0.05286780304627258]</td>\n",
       "      <td>[-0.05171019681143839]</td>\n",
       "      <td>-0.058764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>mnth_8</td>\n",
       "      <td>[0.04607586056694693]</td>\n",
       "      <td>[0.02003899475447709]</td>\n",
       "      <td>0.001111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>mnth_9</td>\n",
       "      <td>[0.15163537217531473]</td>\n",
       "      <td>[0.10126801182431759]</td>\n",
       "      <td>0.088945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>mnth_10</td>\n",
       "      <td>[0.13840354167606672]</td>\n",
       "      <td>[0.054277410313525246]</td>\n",
       "      <td>0.058906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>mnth_11</td>\n",
       "      <td>[0.07538378033548292]</td>\n",
       "      <td>[-0.03526650329981513]</td>\n",
       "      <td>-0.023747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>mnth_12</td>\n",
       "      <td>[0.11560578072768136]</td>\n",
       "      <td>[-0.02363935873138581]</td>\n",
       "      <td>-0.018054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>weathersit_1</td>\n",
       "      <td>[0.06816989057002558]</td>\n",
       "      <td>[0.06701091469396332]</td>\n",
       "      <td>0.087980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>weathersit_2</td>\n",
       "      <td>[-0.02622771508275496]</td>\n",
       "      <td>[-0.025359076901812896]</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>weathersit_3</td>\n",
       "      <td>[-0.12871835634088702]</td>\n",
       "      <td>[-0.12779183103536268]</td>\n",
       "      <td>-0.120234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>weekday_0</td>\n",
       "      <td>[-0.040168280214920166]</td>\n",
       "      <td>[-0.03986332955763131]</td>\n",
       "      <td>-0.049638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>weekday_1</td>\n",
       "      <td>[-0.022032841518754037]</td>\n",
       "      <td>[-0.023409361001580416]</td>\n",
       "      <td>-0.022948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>weekday_2</td>\n",
       "      <td>[-0.005042303246017356]</td>\n",
       "      <td>[-0.004909335236438471]</td>\n",
       "      <td>-0.003628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>weekday_3</td>\n",
       "      <td>[0.002628140810272862]</td>\n",
       "      <td>[0.0029512656197777667]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>weekday_4</td>\n",
       "      <td>[0.014810860819678362]</td>\n",
       "      <td>[0.014855344577688482]</td>\n",
       "      <td>0.007336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>weekday_5</td>\n",
       "      <td>[0.018005349806666703]</td>\n",
       "      <td>[0.019318297275929493]</td>\n",
       "      <td>0.012709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>weekday_6</td>\n",
       "      <td>[0.03155236553254804]</td>\n",
       "      <td>[0.030782644158985278]</td>\n",
       "      <td>0.013862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>temp</td>\n",
       "      <td>[0.3509956339033794]</td>\n",
       "      <td>[0.2511189971324577]</td>\n",
       "      <td>0.349194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>atemp</td>\n",
       "      <td>[0.11147710322898721]</td>\n",
       "      <td>[0.18975529513273853]</td>\n",
       "      <td>0.114613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>hum</td>\n",
       "      <td>[-0.13817231583763126]</td>\n",
       "      <td>[-0.1365408991663952]</td>\n",
       "      <td>-0.135913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>[-0.1240675310920183]</td>\n",
       "      <td>[-0.12076456088391704]</td>\n",
       "      <td>-0.120262</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         columns                  lr_coef               ridge_coef  lasso_coef\n",
       "0         dteday    [-0.3607766672373165]   [-0.05881451589172962]   -0.000000\n",
       "1             yr     [0.5120802216168161]      [0.503631314007401]    0.506015\n",
       "2        holiday   [-0.02740068565607052]  [-0.025884476719685145]   -0.029918\n",
       "3     workingday   [0.016168900910427116]   [0.015982578925309198]    0.000000\n",
       "4       season_1   [-0.18450204625098793]   [-0.17798071622643852]   -0.216443\n",
       "5       season_2   [0.012164414064620257]   [0.012933827866272445]    0.000000\n",
       "6       season_3    [0.01718282238980799]   [0.015536941977268865]   -0.000000\n",
       "7       season_4    [0.15239503338341406]    [0.14683173021639442]    0.096102\n",
       "8         mnth_1   [-0.19501544643340768]   [-0.07457439595345973]   -0.022242\n",
       "9         mnth_2     [-0.146271269781228]  [-0.052073684635153675]   -0.008009\n",
       "10        mnth_3    [-0.0685000798268201]     [0.0126897051510936]    0.040466\n",
       "11        mnth_4   [-0.07023248098438316]  [-0.011317007299342822]    0.000000\n",
       "12        mnth_5    [0.01606616957287441]    [0.05641011697633028]    0.056655\n",
       "13        mnth_6  [-0.016965310919035533]  [0.0032741290733800444]   -0.000000\n",
       "14        mnth_7   [-0.05286780304627258]   [-0.05171019681143839]   -0.058764\n",
       "15        mnth_8    [0.04607586056694693]    [0.02003899475447709]    0.001111\n",
       "16        mnth_9    [0.15163537217531473]    [0.10126801182431759]    0.088945\n",
       "17       mnth_10    [0.13840354167606672]   [0.054277410313525246]    0.058906\n",
       "18       mnth_11    [0.07538378033548292]   [-0.03526650329981513]   -0.023747\n",
       "19       mnth_12    [0.11560578072768136]   [-0.02363935873138581]   -0.018054\n",
       "20  weathersit_1    [0.06816989057002558]    [0.06701091469396332]    0.087980\n",
       "21  weathersit_2   [-0.02622771508275496]  [-0.025359076901812896]   -0.000000\n",
       "22  weathersit_3   [-0.12871835634088702]   [-0.12779183103536268]   -0.120234\n",
       "23     weekday_0  [-0.040168280214920166]   [-0.03986332955763131]   -0.049638\n",
       "24     weekday_1  [-0.022032841518754037]  [-0.023409361001580416]   -0.022948\n",
       "25     weekday_2  [-0.005042303246017356]  [-0.004909335236438471]   -0.003628\n",
       "26     weekday_3   [0.002628140810272862]  [0.0029512656197777667]    0.000000\n",
       "27     weekday_4   [0.014810860819678362]   [0.014855344577688482]    0.007336\n",
       "28     weekday_5   [0.018005349806666703]   [0.019318297275929493]    0.012709\n",
       "29     weekday_6    [0.03155236553254804]   [0.030782644158985278]    0.013862\n",
       "30          temp     [0.3509956339033794]     [0.2511189971324577]    0.349194\n",
       "31         atemp    [0.11147710322898721]    [0.18975529513273853]    0.114613\n",
       "32           hum   [-0.13817231583763126]    [-0.1365408991663952]   -0.135913\n",
       "33     windspeed    [-0.1240675310920183]   [-0.12076456088391704]   -0.120262"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(feat_names), \"lr_coef\":list(lr.coef_.T),\"ridge_coef\":list(ridge.coef_.T),\"lasso_coef\":list(lasso.coef_.T)})\n",
    "fs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lasso模型增加了L1正则，系数值进行了收缩，同时有些特征的系数为0。\n",
    "在这个例子中，岭回归模型比Lasso模型性能稍好。"
   ]
  }
 ],
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